Yearly Traffic Safety Analysis

1,093 CRASHES IN
OHIO, OH
2023

All metrics benchmarked against2022

In 2023, Ashland County recorded 1,093 total crashes, a 12.5% decrease from the 1,249 crashes documented in 2022. The most significant year-over-year shift was a 50% reduction in traffic fatalities, which fell from 10 in the prior year to 5 in the current year. The number of injuries also decreased by 11.4%, from 492 to 436.

1,093

▼ -12.5%was 1,249

Total Crash Events

5

▼ -50.0%was 10

Persons Killed

436

▼ -11.4%was 492

Persons Injured

79

▼ -2.5%was 81

Hit-and-Run Crashes

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety metrics in Ashland County showed a downward trend in 2023 compared to the previous year. Total crashes fell by 12.5%, from 1,249 to 1,093. This decline was mirrored in crash outcomes, with total injuries decreasing by 11.4% and fatalities dropping by 50%.

79

Hit-and-Run Crashes — 2023

▼ -2.5% vs prior (81)

While the absolute number of hit-and-run incidents was stable, decreasing slightly from 81 in 2022 to 79 in 2023, the rate of such crashes trended upward. As a percentage of all crashes, hit-and-runs rose from 6.5% in the prior period to 7.2% in the current period, owing to the larger overall decrease in total crashes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

5

Motorists Killed

Prior: 9↓-44.4%

5

Pedestrians Injured

Prior: 5→0.0%

431

Motorists Injured

Prior: 487↓-11.5%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Friday (226 crashes) in 2022 to Thursday (182 crashes) in 2023. However, the peak hour for collisions remained consistent year-over-year, occurring at 4 p.m. in both periods. The number of crashes during the peak hour saw a slight decrease from 91 in 2022 to 87 in 2023.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The fatal crash rate decreased from 0.64% of all crashes in 2022 to 0.46% in 2023, with the count of fatal crashes falling from 8 to 5. The overall proportion of crashes involving any injury remained stable, moving from 26.6% in 2022 to 27.0% in 2023. Within injury categories, the share of serious injury crashes increased slightly from 3.4% to 3.7%, while the proportion of crashes with possible injuries decreased from 8.3% to 7.3%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.5%
↓-37.5%prior 8
Serious Injury40serious injury crashes3.7%
↓-4.8%prior 42
Minor Injury175minor injury crashes16%
↓-5.9%prior 186
Possible Injury80possible injury crashes7.3%
↓-23.1%prior 104
No Injury793no injury crashes72.6%
↓-12.8%prior 909

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather on dry roads. However, the proportion of crashes on unlit dark roadways increased from 22.7% of all crashes in 2022 to 28.1% in 2023. Similarly, the share of collisions on wet roads rose from 14.9% to 17.1% year-over-year, while crashes in daylight conditions decreased proportionally from 62.3% to 59.5%.

Weather

Clear701 (64.1%)
↓-6.8%prior 752
Cloudy209 (19.1%)
↓-23.7%prior 274
Rain106 (9.7%)
↑19.1%prior 89
Snow55 (5.0%)
↓-40.9%prior 93
Fog; Smog; Smoke11 (1.0%)
↑22.2%prior 9
Other/Unknown4 (0.4%)
↓-63.6%prior 11
Sleet; Hail4 (0.4%)
Severe Crosswinds2 (0.2%)
↓-71.4%prior 7
Freezing Rain or Freezing Drizzle1 (0.1%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight650 (59.5%)
↓-16.5%prior 778
Dark - Roadway Not Lighted307 (28.1%)
↑8.1%prior 284
Dawn/Dusk65 (5.9%)
↓-32.3%prior 96
Dark - Lighted Roadway57 (5.2%)
↓-20.8%prior 72
Dark - Unknown Roadway Lighting10 (0.9%)
↑11.1%prior 9
Other/Unknown4 (0.4%)
↓-60.0%prior 10

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry848 (77.6%)
↓-10.2%prior 944
Wet187 (17.1%)
↑0.5%prior 186
Snow37 (3.4%)
↓-51.3%prior 76
Ice11 (1.0%)
↓-65.6%prior 32
Water (Standing; Moving)5 (0.5%)
Slush3 (0.3%)
Other/Unknown2 (0.2%)
↓-60.0%prior 5

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The types of vehicles involved in crashes and their manufacturers remained consistent year-over-year, with Passenger Cars, Sport Utility Vehicles, and Pick-ups being the most common vehicle types. Ford, Chevrolet, and Honda were the top three makes involved in crashes in both periods. Analysis of persons involved in crashes shows a proportional decrease in the 65+ age group, which accounted for 13.2% of individuals in 2022 but 11.2% in 2023.

Top Vehicle Makes (1,715 vehicles)

1
FORD304 (17.7%)
↓-11.6%prior 344
2
CHEVROLET298 (17.4%)
↓-1.3%prior 302
3
HONDA128 (7.5%)
↓-16.3%prior 153
4
TOYOTA125 (7.3%)
↓-0.8%prior 126
5
DODGE103 (6%)
↓-9.6%prior 114
6
JEEP80 (4.7%)
↓-21.6%prior 102
7
KIA63 (3.7%)
↓-18.2%prior 77
8
NISSAN55 (3.2%)
↓-21.4%prior 70
9
HYUNDAI54 (3.1%)
↓-3.6%prior 56
10
FREIGHTLINER40 (2.3%)
↓-13.0%prior 46

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

62 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (2,272 persons with recorded sex)

Male1,284 (56.5%)
↓-12.2%prior 1,462
Female988 (43.5%)
↓-13.4%prior 1,141

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Ohio Crash Data (ODOT TIMS), accessed programmatically via the Csv Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: Csv Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2023-01-01 through 2023-12-31
  • Report generated: July 6, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 1,093
  • Total persons involved: 2,324
  • Total vehicles involved: 1,715

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "ohio, OH Crash Intelligence Report: 2023." Published July 6, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/2023-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

ThatCarHitMe.com · An Injuria.ai Company

Ashland County, OH Crash Report — 2023 | ThatCarHitMe.com